# YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count

arXiv: https://arxiv.org/abs/2405.02312 · HTML: https://mlsanity.com/paper/2405.02312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [DeepSeaNet: Improving Underwater Object Detection using EfficientDet](https://mlsanity.com/paper/2306.06075/) (2023)
- [UTD-Yolov5: A Real-time Underwater Targets Detection Method based on Attention Improved YOLOv5](https://mlsanity.com/paper/2207.00837/) (2022)

## Built on by (most cited first, 1 of 1)

- [Accelerating Local AI on Consumer GPUs: A Hardware-Aware Dynamic Strategy for YOLOv10s](https://mlsanity.com/paper/2509.07928/) (2025)

## Abstract (arXiv)

This paper presents a comparative study of object detection using YOLOv5 and YOLOv8 for three distinct classes: artemia, cyst, and excrement. In this comparative study, we analyze the performance of these models in terms of accuracy, precision, recall, etc. where YOLOv5 often performed better in detecting Artemia and cysts with excellent precision and accuracy. However, when it came to detecting excrement, YOLOv5 faced notable challenges and limitations. This suggests that YOLOv8 offers greater versatility and adaptability in detection tasks while YOLOv5 may struggle in difficult situations and may need further fine-tuning or specialized training to enhance its performance. The results show insights into the suitability of YOLOv5 and YOLOv8 for detecting objects in challenging marine environments, with implications for applications such as ecological research.